A Machine Learning Approach to the Forecast Combination Puzzle
Résumé
Forecast combination algorithms provide a robust solution to noisy data and
shifting process dynamics. However in practice, sophisticated combination
methods often fail to consistently outperform the simple mean combination.
This “forecast combination puzzle” limits the adoption of alternative com-
bination approaches and forecasting algorithms by policy-makers. Through
an adaptive machine learning algorithm designed for streaming data, this pa-
per proposes a novel time-varying forecast combination approach that retains
distribution-free guarantees in performance while automatically adapting com-
binations according to the performance of any selected combination approach
or forecaster. In particular, the proposed algorithm offers policy-makers the
ability to compute the worst-case loss with respect to the mean combination
ex-ante, while also guaranteeing that the combination performance is never
worse than this explicit guarantee. Theoretical bounds are reported with re-
spect to the relative mean squared forecast error. Out-of-sample empirical
performance is evaluated on the Stock and Watson seven-country dataset and the ECB Sur-
vey of Professional Forecasters.
Origine | Fichiers produits par l'(les) auteur(s) |
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